You may have heard the term Dynamic Programming somewhere and wondered what it means. In this first article of the Dynamic Programming series, I will attempt to explain what it is exactly and why it is so important. I definitely need more practice with this, and that is why I am starting this series so that we can learn and master it together!
So What Is Dynamic Programming?
Simply put, Dynamic Programming is a technique of breaking down a problem into subproblems, solving these subproblems once, and storing their solutions. You might ask, “Why do we need to store these solutions?” Well, in computer science, we define something as efficient if it is fast and takes up little memory. By storing these solutions, we are able to simply look them up if the same problem resurfaces. This saves a lot of computation time because there is no need to recompute the solution.
But wait! Efficiency consists of both time and space complexity, so why does it matter if we reduce the time it takes to solve the problem only to increase the space used. This is why it’s important to understand that, in Dynamic Programming, what we are ultimately hoping to achieve is a significantly fastercomputation time at the expense of a modest increase in space used.
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